%run "/code/source/notebooks/notebook_settings.py"
import logging
import numpy as np
import helpsk as hlp
from helpsk.utility import read_pickle, Timer
from helpsk.sklearn_eval import MLExperimentResults

import source.config.config as config
from source.service.model_registry import ModelRegistry

logging.info("Running experiment notebook for last run.")

2023-11-10 07:26:30 - INFO     | Running experiment notebook for last run.

Get Latest Experiment Run from MLFlow

registry = ModelRegistry(tracking_uri=config.experiment_server_url())
experiment = registry.get_experiment_by_name(exp_name=config.experiment_name())
logging.info(f"Experiment id: {experiment.last_run.exp_id}")
logging.info(f"Experiment name: {experiment.last_run.exp_name}")
logging.info(f"Run id: {experiment.last_run.run_id}")
logging.info(f"Metric(s): {experiment.last_run.metrics}")

2023-11-10 07:26:30 - INFO     | Experiment id: 1
2023-11-10 07:26:30 - INFO     | Experiment name: credit
2023-11-10 07:26:30 - INFO     | Run id: ddbccbc5d7f647b3ac3d498e19eb6404
2023-11-10 07:26:30 - INFO     | Metric(s): {'roc_auc': 0.7700760706900486}

Last Run vs Production

What is the metric/performance from the model associated with the last run?

logging.info(f"last run metrics: {experiment.last_run.metrics}")

2023-11-10 07:26:30 - INFO     | last run metrics: {'roc_auc': 0.7700760706900486}

What is the metric/performance of the model in production?

production_run = registry.get_production_run(model_name=config.model_name())
logging.info(f"production run metrics: {production_run.metrics}")

2023-11-10 07:26:30 - INFO     | production run metrics: {'roc_auc': 0.7700760706900486}

Last Run

# underlying mlflow object
experiment.last_run.mlflow_entity

<Run: data=<RunData: metrics={'roc_auc': 0.7700760706900486}, params={'model__criterion': 'entropy',
 'model__max_depth': '99',
 'model__max_features': '0.031837350792579364',
 'model__max_samples': '0.9248344222191298',
 'model__min_samples_leaf': '4',
 'model__min_samples_split': '16',
 'model__n_estimators': '1235',
 'prep__non_numeric__encoder__transformer': 'CustomOrdinalEncoder()',
 'prep__numeric__imputer__transformer': "SimpleImputer(strategy='median')",
 'prep__numeric__pca__transformer': "PCA(n_components='mle')",
 'prep__numeric__scaler__transformer': 'None'}, tags={'mlflow.log-model.history': '[{"run_id": "ddbccbc5d7f647b3ac3d498e19eb6404", '
                             '"artifact_path": "model", "utc_time_created": '
                             '"2023-11-10 07:26:26.465365", "flavors": '
                             '{"python_function": {"model_path": "model.pkl", '
                             '"predict_fn": "predict", "loader_module": '
                             '"mlflow.sklearn", "python_version": "3.11.6", '
                             '"env": {"conda": "conda.yaml", "virtualenv": '
                             '"python_env.yaml"}}, "sklearn": '
                             '{"pickled_model": "model.pkl", '
                             '"sklearn_version": "1.3.2", '
                             '"serialization_format": "cloudpickle", "code": '
                             'null}}, "model_uuid": '
                             '"db69564af5ac40d097f6ceca69153ec6", '
                             '"mlflow_version": "2.8.0", "model_size_bytes": '
                             '4523753}]',
 'mlflow.note.content': '2023_11_10_07_26_02',
 'mlflow.runName': '2023_11_10_07_26_02',
 'mlflow.source.git.commit': 'e4a4afc62a952e1fc94d01102f96b08cd2840540',
 'mlflow.source.name': 'source/entrypoints/cli.py',
 'mlflow.source.type': 'LOCAL',
 'mlflow.user': 'root',
 'type': 'BayesSearchCV'}>, info=<RunInfo: artifact_uri='/code/mlflow-artifact-root/1/ddbccbc5d7f647b3ac3d498e19eb6404/artifacts', end_time=1699601188099, experiment_id='1', lifecycle_stage='active', run_id='ddbccbc5d7f647b3ac3d498e19eb6404', run_name='2023_11_10_07_26_02', run_uuid='ddbccbc5d7f647b3ac3d498e19eb6404', start_time=1699601162959, status='FINISHED', user_id='root'>, inputs=<RunInputs: dataset_inputs=[]>>

---

Load Training & Test Data Info

with Timer("Loading training/test datasets"):
    X_train = experiment.last_run.download_artifact(artifact_name='x_train.pkl', read_from=read_pickle)
    X_test = experiment.last_run.download_artifact(artifact_name='x_test.pkl', read_from=read_pickle)
    y_train = experiment.last_run.download_artifact(artifact_name='y_train.pkl', read_from=read_pickle)
    y_test = experiment.last_run.download_artifact(artifact_name='y_test.pkl', read_from=read_pickle)

Timer Started: Loading training/test datasets
Timer Finished (0.01 seconds)

logging.info(f"training X shape: {X_train.shape}")
logging.info(f"training y length: {len(y_train)}")

logging.info(f"test X shape: {X_test.shape}")
logging.info(f"test y length: {len(y_test)}")

2023-11-10 07:26:30 - INFO     | training X shape: (800, 20)
2023-11-10 07:26:30 - INFO     | training y length: 800
2023-11-10 07:26:30 - INFO     | test X shape: (200, 20)
2023-11-10 07:26:30 - INFO     | test y length: 200

np.unique(y_train, return_counts=True)

(array([0, 1]), array([559, 241]))

train_y_proportion = np.unique(y_train, return_counts=True)[1] \
    / np.sum(np.unique(y_train, return_counts=True)[1])
logging.info(f"balance of y in training: {train_y_proportion}")

2023-11-10 07:26:30 - INFO     | balance of y in training: [0.69875 0.30125]

test_y_proportion = np.unique(y_test, return_counts=True)[1] \
    / np.sum(np.unique(y_test, return_counts=True)[1])
logging.info(f"balance of y in test: {test_y_proportion}")

2023-11-10 07:26:30 - INFO     | balance of y in test: [0.705 0.295]

Cross Validation Results

Best Scores/Params

results = experiment.last_run.download_artifact(
    artifact_name='experiment.yaml',
    read_from=MLExperimentResults.from_yaml_file
)
logging.info(f"Best Score: {results.best_score}")
logging.info(f"Best Params: {results.best_params}")

2023-11-10 07:26:30 - INFO     | Best Score: 0.7700760706900486
2023-11-10 07:26:30 - INFO     | Best Params: {'model': 'ExtraTreesClassifier()', 'max_features': 0.031837350792579364, 'max_depth': 99, 'n_estimators': 1235, 'min_samples_split': 16, 'min_samples_leaf': 4, 'max_samples': 0.9248344222191298, 'criterion': 'entropy', 'imputer': "SimpleImputer(strategy='median')", 'scaler': 'None', 'pca': "PCA('mle')", 'encoder': 'CustomOrdinalEncoder()'}

# Best model from each model-type.
df = results.to_formatted_dataframe(return_style=False, include_rank=True)
df["model_rank"] = df.groupby("model")["roc_auc Mean"].rank(method="first", ascending=False)
df.query('model_rank == 1')

	rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	model	C	max_features	max_depth	n_estimators	min_samples_split	min_samples_leaf	max_samples	criterion	learning_rate	min_child_weight	subsample	colsample_bytree	colsample_bylevel	reg_alpha	reg_lambda	num_leaves	imputer	scaler	pca	encoder	model_rank
9	1	0.77	0.73	0.81	ExtraTreesClassifier()	NaN	0.03	99.00	1235.00	16.00	4.00	0.92	entropy	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()	1.00
3	2	0.76	0.71	0.81	LogisticRegression()	0.01	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	SimpleImputer()	StandardScaler()	PCA('mle')	OneHotEncoder()	1.00
10	3	0.76	0.70	0.82	RandomForestClassifier()	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	SimpleImputer()	None	None	OneHotEncoder()	1.00
18	6	0.75	0.71	0.79	XGBClassifier()	NaN	NaN	4.00	1414.00	NaN	NaN	NaN	NaN	0.01	1.00	0.84	0.62	0.54	0.78	1.24	NaN	SimpleImputer()	None	PCA('mle')	CustomOrdinalEncoder()	1.00
21	16	0.73	0.68	0.78	LGBMClassifier()	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.61	0.98	NaN	8.89	7.31	243.00	SimpleImputer(strategy='most_frequent')	None	PCA('mle')	OneHotEncoder()	1.00

results.to_formatted_dataframe(return_style=True,
                               include_rank=True,
                               num_rows=500)

rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	model	C	max_features	max_depth	n_estimators	min_samples_split	min_samples_leaf	max_samples	criterion	learning_rate	min_child_weight	subsample	colsample_bytree	colsample_bylevel	reg_alpha	reg_lambda	num_leaves	imputer	scaler	pca	encoder
1	0.770	0.732	0.808	ExtraTreesClassifier()	<NA>	0.032	99.000	1,235.000	16.000	4.000	0.925	entropy	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()
2	0.762	0.714	0.810	LogisticRegression()	0.014	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	StandardScaler()	PCA('mle')	OneHotEncoder()
3	0.761	0.702	0.820	RandomForestClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
4	0.759	0.712	0.806	LogisticRegression()	0.000	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='most_frequent')	MinMaxScaler()	None	OneHotEncoder()
5	0.757	0.711	0.803	LogisticRegression()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	StandardScaler()	None	OneHotEncoder()
6	0.751	0.708	0.793	XGBClassifier()	<NA>	<NA>	4.000	1,414.000	<NA>	<NA>	<NA>	<NA>	0.013	1.000	0.841	0.624	0.542	0.784	1.243	<NA>	SimpleImputer()	None	PCA('mle')	CustomOrdinalEncoder()
7	0.749	0.724	0.774	ExtraTreesClassifier()	<NA>	0.563	71.000	1,725.000	49.000	16.000	0.956	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	None	CustomOrdinalEncoder()
8	0.746	0.698	0.794	ExtraTreesClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
9	0.745	0.703	0.787	RandomForestClassifier()	<NA>	0.757	44.000	745.000	33.000	6.000	0.608	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	CustomOrdinalEncoder()
10	0.743	0.694	0.793	XGBClassifier()	<NA>	<NA>	2.000	1,671.000	<NA>	<NA>	<NA>	<NA>	0.021	2.000	0.657	0.591	0.780	0.026	3.081	<NA>	SimpleImputer(strategy='median')	None	None	OneHotEncoder()
11	0.743	0.687	0.798	XGBClassifier()	<NA>	<NA>	5.000	1,157.000	<NA>	<NA>	<NA>	<NA>	0.018	3.000	0.694	0.501	0.726	0.033	2.910	<NA>	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()
12	0.738	0.727	0.748	ExtraTreesClassifier()	<NA>	0.861	52.000	1,995.000	33.000	19.000	0.651	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='most_frequent')	None	None	CustomOrdinalEncoder()
13	0.737	0.716	0.757	RandomForestClassifier()	<NA>	0.528	70.000	1,003.000	37.000	19.000	0.530	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()
14	0.734	0.709	0.758	RandomForestClassifier()	<NA>	0.422	68.000	1,348.000	4.000	39.000	0.859	entropy	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()
15	0.733	0.705	0.760	ExtraTreesClassifier()	<NA>	0.666	19.000	1,346.000	6.000	44.000	0.852	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	PCA('mle')	OneHotEncoder()
16	0.732	0.682	0.782	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	0.612	0.979	<NA>	8.891	7.306	243.000	SimpleImputer(strategy='most_frequent')	None	PCA('mle')	OneHotEncoder()
17	0.731	0.689	0.772	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	0.338	0.617	<NA>	10.572	26.393	255.000	SimpleImputer(strategy='median')	None	PCA('mle')	OneHotEncoder()
18	0.729	0.645	0.814	LogisticRegression()	0.000	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='most_frequent')	StandardScaler()	PCA('mle')	OneHotEncoder()
19	0.727	0.694	0.759	RandomForestClassifier()	<NA>	0.940	40.000	899.000	3.000	16.000	0.696	entropy	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	None	CustomOrdinalEncoder()
20	0.723	0.705	0.741	LogisticRegression()	0.000	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	StandardScaler()	None	CustomOrdinalEncoder()
21	0.722	0.677	0.767	XGBClassifier()	<NA>	<NA>	13.000	1,222.000	<NA>	<NA>	<NA>	<NA>	0.080	5.000	0.825	0.700	0.977	0.020	1.105	<NA>	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()
22	0.719	0.628	0.810	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
23	0.718	0.682	0.753	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	0.925	0.859	<NA>	14.754	33.903	400.000	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()
24	0.710	0.663	0.756	XGBClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
25	0.702	0.683	0.721	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	0.374	0.990	<NA>	16.223	18.789	378.000	SimpleImputer()	None	PCA('mle')	CustomOrdinalEncoder()

results.to_formatted_dataframe(query='model == "RandomForestClassifier()"', include_rank=True)

rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	max_features	max_depth	n_estimators	min_samples_split	min_samples_leaf	max_samples	criterion	imputer	pca	encoder
1	0.761	0.702	0.820	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	OneHotEncoder()
2	0.745	0.703	0.787	0.757	44.000	745.000	33.000	6.000	0.608	gini	SimpleImputer()	None	CustomOrdinalEncoder()
3	0.737	0.716	0.757	0.528	70.000	1,003.000	37.000	19.000	0.530	gini	SimpleImputer(strategy='median')	PCA('mle')	CustomOrdinalEncoder()
4	0.734	0.709	0.758	0.422	68.000	1,348.000	4.000	39.000	0.859	entropy	SimpleImputer(strategy='median')	PCA('mle')	CustomOrdinalEncoder()
5	0.727	0.694	0.759	0.940	40.000	899.000	3.000	16.000	0.696	entropy	SimpleImputer(strategy='median')	None	CustomOrdinalEncoder()

results.to_formatted_dataframe(query='model == "LogisticRegression()"', include_rank=True)

rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	C	imputer	scaler	pca	encoder
1	0.762	0.714	0.810	0.014	SimpleImputer()	StandardScaler()	PCA('mle')	OneHotEncoder()
2	0.759	0.712	0.806	0.000	SimpleImputer(strategy='most_frequent')	MinMaxScaler()	None	OneHotEncoder()
3	0.757	0.711	0.803	<NA>	SimpleImputer()	StandardScaler()	None	OneHotEncoder()
4	0.729	0.645	0.814	0.000	SimpleImputer(strategy='most_frequent')	StandardScaler()	PCA('mle')	OneHotEncoder()
5	0.723	0.705	0.741	0.000	SimpleImputer(strategy='median')	StandardScaler()	None	CustomOrdinalEncoder()

BayesSearchCV Performance Over Time

results.plot_performance_across_trials(facet_by='model').show()

results.plot_performance_across_trials(query='model == "RandomForestClassifier()"').show()

---

Variable Performance Over Time

results.plot_parameter_values_across_trials(query='model == "RandomForestClassifier()"').show()

---

Scatter Matrix

# results.plot_scatter_matrix(query='model == "RandomForestClassifier()"',
#                             height=1000, width=1000).show()

---

Variable Performance - Numeric

results.plot_performance_numeric_params(query='model == "RandomForestClassifier()"',
                                        height=800)

results.plot_parallel_coordinates(query='model == "RandomForestClassifier()"').show()

---

Variable Performance - Non-Numeric

results.plot_performance_non_numeric_params(query='model == "RandomForestClassifier()"').show()

---

results.plot_score_vs_parameter(
    query='model == "RandomForestClassifier()"',
    parameter='max_features',
    size='max_depth',
    color='encoder',
)

---

# results.plot_parameter_vs_parameter(
#     query='model == "XGBClassifier()"',
#     parameter_x='colsample_bytree',
#     parameter_y='learning_rate',
#     size='max_depth'
# )

# results.plot_parameter_vs_parameter(
#     query='model == "XGBClassifier()"',
#     parameter_x='colsample_bytree',
#     parameter_y='learning_rate',
#     size='imputer'
# )

---

Last Run - Test Set Performance

last_model = experiment.last_run.download_artifact(
    artifact_name='model/model.pkl',
    read_from=read_pickle
)
print(type(last_model.model))

<class 'sklearn.pipeline.Pipeline'>

last_model

SklearnModelWrapper(model=Pipeline(steps=[('prep',
                                           ColumnTransformer(transformers=[('numeric',
                                                                            Pipeline(steps=[('imputer',
                                                                                             TransformerChooser(transformer=SimpleImputer(strategy='median'))),
                                                                                            ('scaler',
                                                                                             TransformerChooser()),
                                                                                            ('pca',
                                                                                             TransformerChooser(transformer=PCA(n_components='mle')))]),
                                                                            ['duration',
                                                                             'credit_amount',
                                                                             'installment_commitment',
                                                                             'residence_si...
                                                                             'personal_status',
                                                                             'other_parties',
                                                                             'property_magnitude',
                                                                             'other_payment_plans',
                                                                             'housing',
                                                                             'job',
                                                                             'own_telephone',
                                                                             'foreign_worker'])])),
                                          ('model',
                                           ExtraTreesClassifier(bootstrap=True,
                                                                criterion='entropy',
                                                                max_depth=99,
                                                                max_features=0.031837350792579364,
                                                                max_samples=0.9248344222191298,
                                                                min_samples_leaf=4,
                                                                min_samples_split=16,
                                                                n_estimators=1235,
                                                                random_state=42))]))

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

test_predictions = last_model.predict(X_test)
test_predictions[0:10]

array([0.32794716, 0.32893009, 0.37430263, 0.3052219 , 0.23870015,
       0.31131838, 0.24759746, 0.33864031, 0.23092148, 0.24922654])

evaluator = hlp.sklearn_eval.TwoClassEvaluator(
    actual_values=y_test,
    predicted_scores=test_predictions,
    score_threshold=0.37
)

evaluator.plot_actual_vs_predict_histogram()

evaluator.plot_confusion_matrix()

evaluator.all_metrics_df(return_style=True,
                         dummy_classifier_strategy=['prior', 'constant'],
                         round_by=3)

	Score	Dummy (prior)	Dummy (constant)	Explanation
AUC	0.781	0.500	0.500	Area under the ROC curve (true pos. rate vs false pos. rate); ranges from 0.5 (purely random classifier) to 1.0 (perfect classifier)
True Positive Rate	0.169	0.000	1.000	16.9% of positive instances were correctly identified.; i.e. 10 "Positive Class" labels were correctly identified out of 59 instances; a.k.a Sensitivity/Recall
True Negative Rate	0.957	1.000	0.000	95.7% of negative instances were correctly identified.; i.e. 135 "Negative Class" labels were correctly identified out of 141 instances
False Positive Rate	0.043	0.000	1.000	4.3% of negative instances were incorrectly identified as positive; i.e. 6 "Negative Class" labels were incorrectly identified as "Positive Class", out of 141 instances
False Negative Rate	0.831	1.000	0.000	83.1% of positive instances were incorrectly identified as negative; i.e. 49 "Positive Class" labels were incorrectly identified as "Negative Class", out of 59 instances
Positive Predictive Value	0.625	0.000	0.295	When the model claims an instance is positive, it is correct 62.5% of the time; i.e. out of the 16 times the model predicted "Positive Class", it was correct 10 times; a.k.a precision
Negative Predictive Value	0.734	0.705	0.000	When the model claims an instance is negative, it is correct 73.4% of the time; i.e. out of the 184 times the model predicted "Negative Class", it was correct 135 times
F1 Score	0.267	0.000	0.456	The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0.
Precision/Recall AUC	0.621	0.295	0.295	Precision/Recall AUC is calculated with `average_precision` which summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold. See sci-kit learn documentation for caveats.
Accuracy	0.725	0.705	0.295	72.5% of instances were correctly identified
Error Rate	0.275	0.295	0.705	27.5% of instances were incorrectly identified
% Positive	0.295	0.295	0.295	29.5% of the data are positive; i.e. out of 200 total observations; 59 are labeled as "Positive Class"
Total Observations	200	200	200	There are 200 total observations; i.e. sample size

evaluator.plot_roc_auc_curve().show()

evaluator.plot_precision_recall_auc_curve().show()

evaluator.plot_threshold_curves(score_threshold_range=(0.1, 0.7)).show()

evaluator.plot_precision_recall_tradeoff(score_threshold_range=(0.1, 0.6)).show()

evaluator.calculate_lift_gain(return_style=True)

/usr/local/lib/python3.11/site-packages/helpsk/sklearn_eval.py:2480: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

	Gain	Lift
Percentile
5	0.17	3.39
10	0.20	2.03
15	0.34	2.26
20	0.44	2.20
25	0.49	1.97
30	0.56	1.86
35	0.63	1.79
40	0.69	1.74
45	0.76	1.69
50	0.80	1.59
55	0.83	1.51
60	0.85	1.41
65	0.86	1.33
70	0.93	1.33
75	0.95	1.27
80	0.97	1.21
85	0.98	1.16
90	1.00	1.11
95	1.00	1.05
100	1.00	1.00

---

Production Model - Test Set Performance

production_model = production_run.download_artifact(
    artifact_name='model/model.pkl',
    read_from=read_pickle
)
print(type(production_model.model))

<class 'sklearn.pipeline.Pipeline'>

production_model

SklearnModelWrapper(model=Pipeline(steps=[('prep',
                                           ColumnTransformer(transformers=[('numeric',
                                                                            Pipeline(steps=[('imputer',
                                                                                             TransformerChooser(transformer=SimpleImputer(strategy='median'))),
                                                                                            ('scaler',
                                                                                             TransformerChooser()),
                                                                                            ('pca',
                                                                                             TransformerChooser(transformer=PCA(n_components='mle')))]),
                                                                            ['duration',
                                                                             'credit_amount',
                                                                             'installment_commitment',
                                                                             'residence_si...
                                                                             'personal_status',
                                                                             'other_parties',
                                                                             'property_magnitude',
                                                                             'other_payment_plans',
                                                                             'housing',
                                                                             'job',
                                                                             'own_telephone',
                                                                             'foreign_worker'])])),
                                          ('model',
                                           ExtraTreesClassifier(bootstrap=True,
                                                                criterion='entropy',
                                                                max_depth=99,
                                                                max_features=0.031837350792579364,
                                                                max_samples=0.9248344222191298,
                                                                min_samples_leaf=4,
                                                                min_samples_split=16,
                                                                n_estimators=1235,
                                                                random_state=42))]))

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

test_predictions = production_model.predict(X_test)
test_predictions[0:10]

array([0.32794716, 0.32893009, 0.37430263, 0.3052219 , 0.23870015,
       0.31131838, 0.24759746, 0.33864031, 0.23092148, 0.24922654])

evaluator = hlp.sklearn_eval.TwoClassEvaluator(
    actual_values=y_test,
    predicted_scores=test_predictions,
    score_threshold=0.37
)

evaluator.plot_actual_vs_predict_histogram()

evaluator.plot_confusion_matrix()

evaluator.all_metrics_df(return_style=True,
                         dummy_classifier_strategy=['prior', 'constant'],
                         round_by=3)

	Score	Dummy (prior)	Dummy (constant)	Explanation
AUC	0.781	0.500	0.500	Area under the ROC curve (true pos. rate vs false pos. rate); ranges from 0.5 (purely random classifier) to 1.0 (perfect classifier)
True Positive Rate	0.169	0.000	1.000	16.9% of positive instances were correctly identified.; i.e. 10 "Positive Class" labels were correctly identified out of 59 instances; a.k.a Sensitivity/Recall
True Negative Rate	0.957	1.000	0.000	95.7% of negative instances were correctly identified.; i.e. 135 "Negative Class" labels were correctly identified out of 141 instances
False Positive Rate	0.043	0.000	1.000	4.3% of negative instances were incorrectly identified as positive; i.e. 6 "Negative Class" labels were incorrectly identified as "Positive Class", out of 141 instances
False Negative Rate	0.831	1.000	0.000	83.1% of positive instances were incorrectly identified as negative; i.e. 49 "Positive Class" labels were incorrectly identified as "Negative Class", out of 59 instances
Positive Predictive Value	0.625	0.000	0.295	When the model claims an instance is positive, it is correct 62.5% of the time; i.e. out of the 16 times the model predicted "Positive Class", it was correct 10 times; a.k.a precision
Negative Predictive Value	0.734	0.705	0.000	When the model claims an instance is negative, it is correct 73.4% of the time; i.e. out of the 184 times the model predicted "Negative Class", it was correct 135 times
F1 Score	0.267	0.000	0.456	The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0.
Precision/Recall AUC	0.621	0.295	0.295	Precision/Recall AUC is calculated with `average_precision` which summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold. See sci-kit learn documentation for caveats.
Accuracy	0.725	0.705	0.295	72.5% of instances were correctly identified
Error Rate	0.275	0.295	0.705	27.5% of instances were incorrectly identified
% Positive	0.295	0.295	0.295	29.5% of the data are positive; i.e. out of 200 total observations; 59 are labeled as "Positive Class"
Total Observations	200	200	200	There are 200 total observations; i.e. sample size

evaluator.plot_roc_auc_curve().show()

evaluator.plot_precision_recall_auc_curve().show()

evaluator.plot_threshold_curves(score_threshold_range=(0.1, 0.7)).show()

evaluator.plot_precision_recall_tradeoff(score_threshold_range=(0.1, 0.6)).show()

evaluator.calculate_lift_gain(return_style=True)

/usr/local/lib/python3.11/site-packages/helpsk/sklearn_eval.py:2480: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

	Gain	Lift
Percentile
5	0.17	3.39
10	0.20	2.03
15	0.34	2.26
20	0.44	2.20
25	0.49	1.97
30	0.56	1.86
35	0.63	1.79
40	0.69	1.74
45	0.76	1.69
50	0.80	1.59
55	0.83	1.51
60	0.85	1.41
65	0.86	1.33
70	0.93	1.33
75	0.95	1.27
80	0.97	1.21
85	0.98	1.16
90	1.00	1.11
95	1.00	1.05
100	1.00	1.00

---